Aluminum Cross-Car Beam is significantly lighter than the conventional steel counterpart and presents superior energy absorption characteristics. The challenge is however, its considerably higher cost, rendering it difficult for the aluminum one to compete in the automotive market. In this work, using material distribution techniques and stochastic optimization, a Multidisciplinary Design Optimization procedure is developed to optimize an existing Cross-Car Beam model with respect to the cost. Topology, Topography, and gauge optimizations are employed in the development of the optimization disciplines. Based on a qualitative cost assessment, penalty functions are designed to penalize costly designs. Noise-Vibration-Harshness (NVH) performance is the key constraint of the optimization. To fulfill this requirement, natural frequencies are obtained using modal analysis. Undesirable designs with respect to the NVH criteria are gradually eliminated from the optimization cycles. The new design is verified by static loading scenario and evaluated in terms of the cost saving it offers.

Improvements in performance, reliability and durability as well as reductions in production costs, remain critical prerequisites for the commercialization of proton exchange membrane fuel cells. In this thesis, a computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon's algorithm and an adaptive finite element method in order to achieve quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve two optimization problems: i) maximize performance; and, ii) maximize performance while minimizing the production cost of the MEA. To solve these problems a gradient-based optimization algorithm is used in conjunction with analytical sensitivities. The presented computational framework is the first attempt in the literature to combine highly efficient analysis and optimization methods to perform optimization in order to tackle large-scale problems. The framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 minutes. The optimization results show that it is possible to achieve Pt-specific power density for the optimized MEAs of 0.422 $g_{Pt}/kW$. This value is extremely close to the target of 0.4 $g_{Pt}/kW$ for large-scale implementation and demonstrate the potential of using numerical optimization for fuel cell design.

Computational models and simulations are essential system design tools that allow for improved decision making and cost reductions during all phases of the design process. However, the most accurate models are often computationally expensive and can therefore only be used sporadically. Consequently, designers are often forced to choose between exploring many design alternatives with less accurate, inexpensive models and evaluating fewer alternatives with the most accurate models. To achieve both broad exploration of the alternatives and accurate determination of the best alternative with reasonable costs incurred, surrogate modeling and variable accuracy modeling are used widely. A surrogate model is a mathematically tractable approximation of a more expensive model based on a limited sampling of that model, while variable accuracy modeling involves a collection of different models of the same system with different accuracies and computational costs. As compared to using only very accurate and expensive models, designers can determine the best solutions more efficiently using surrogate and variable accuracy models because obviously poor solutions can be eliminated inexpensively using only the less expensive, less accurate models. The most accurate models are then reserved for discerning the best solution from the set of good solutions. In this thesis, a Value-Based Global Optimization (VGO) algorithm is introduced. The algorithm uses kriging-like surrogate models and a sequential sampling strategy based on Value of Information (VoI) to optimize an objective characterized by multiple analysis models with different accuracies. It builds on two primary research contributions. The first is a novel surrogate modeling method that accommodates data from any number of analysis models with different accuracies and costs. The second contribution is the use of Value of Information (VoI) as a new metric for guiding the sequential sampling process for global optimization. In this manner, the cost of further analysis is explicitly taken into account during the optimization process. Results characterizing the algorithm show that VGO outperforms Efficient Global Optimization (EGO), a similar global optimization algorithm that is considered to be the current state of the art. It is shown that when cost is taken into account in the final utility, VGO achieves a higher utility than EGO with statistical significance. In further experiments, it is shown that VGO can be successfully applied to higher dimensional problems as well as practical engineering design examples.

Improvements in performance, reliability and durability as well as reductions in production costs, remain critical prerequisites for the commercialization of proton exchange membrane fuel cells. In this thesis, a computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon's algorithm and an adaptive finite element method in order to achieve quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve two optimization problems: i) maximize performance; and, ii) maximize performance while minimizing the production cost of the MEA. To solve these problems a gradient-based optimization algorithm is used in conjunction with analytical sensitivities. The presented computational framework is the first attempt in the literature to combine highly efficient analysis and optimization methods to perform optimization in order to tackle large-scale problems. The framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 minutes. The optimization results show that it is possible to achieve Pt-specific power density for the optimized MEAs of 0.422 $g_{Pt}/kW$. This value is extremely close to the target of 0.4 $g_{Pt}/kW$ for large-scale implementation and demonstrate the potential of using numerical optimization for fuel cell design.

A indústria aeronáutica vem promovendo avanços tecnológicos em velocidades crescentes, para sobreviver em mercados extremamente competitivos. Neste cenário, torna-se imprescindível o uso de ferramentas de projeto que agilizem o desenvolvimento de novas aeronaves. Os atuais recursos computacionais permitiram um grande aumento no número de ferramentas que auxiliam o trabalho de projetistas e engenheiros. O projeto de uma aeronave é uma tarefa multidisciplinar por essência, o que logo incentivou o desenvolvimento de ferramentas computacionais que trabalhem com várias áreas ao mesmo tempo. Entre elas se destaca a otimização multidisciplinar em projeto, que une métodos de otimização à modelos matemáticos de áreas distintas de um projeto para encontrar soluções de compromisso. O presente trabalho introduz a otimização multidisciplinar em projeto (Multidisciplinary Design Optimization - MDO) e discorre sobre algumas aplicações possíveis desta metodologia. Foi realizada a implementação de um sistema de MDO para o projeto de asas flexíveis, considerando restrições de aeroelasticidade dinâmica e massa estrutural. Como meta, deseja-se encontrar distribuições ideais de rigidezes flexional e torcional da estrutura da asa, para maximizar a velocidade crítica de flutter e minimizar a massa estrutural. Para tanto, foram utilizados um modelo dinâmico-estrutural baseado no método dos elementos finitos, um modelo aerodinâmico não-estacionário baseado na teoria das faixas e nas soluções bidimensionais de Theodorsen, um modelo de previsão de flutter que utiliza o método K e, por fim, um otimizador baseado no método de algoritmos genéticos (AGs). São apresentados os detalhes empregados em cada modelo, as restrições aplicadas e a maneira como eles interagem ao longo da otimização. É feita uma análise para a escolha dos parâmetros de otimização por AG e em seguida a avaliação de dois casos, para verificação da funcionalidade do sistema implementado. Os resultados obtidos demonstram uma metodologia eficiente, que é capaz de buscar soluções ótimas para problemas propostos, que com devidos ajustes pode ter enorme valor para acelerar o desenvolvimento de novas aeronaves. / The aeronautical industry is always trying to speed up technological advances in order to survive in extremely competitive markets. In this scenario, the use of design tools to accelerate the development of new aircraft becomes essential. Current computational resources allow greater increase in the number of design tools to assist the work of aeronautical engineers. In essence, the design of an aircraft is a multidisciplinary task, which stimulates the development of computational tools that work with different areas at the same time. Among them, the multidisciplinary design optimization (MDO) can be distinguished, which combines optimization methods to mathematical models of distinct areas of a design to find compromise solutions. The present work introduces MDO and discourses on some possible applications of this methodology. The implementation of a MDO system for the design of flexible wings, considering dynamic aeroelasticity restrictions and the structural mass, was carried out. As goal, it is desired to find ideal flexional and torsional stiffness distributions of the wing structure, that maximize the critical flutter speed and minimize the structural mass. To do so, it was employed a structural dynamics model based on the finite element method, a nonstationary aerodynamic model based on the strip theory and Theodorsen’s two-dimensional solutions, a flutter prediction model based on the K method and a genetic algorithm (GA). Details on the model, restrictions applied and the way the models interact to each other through the optimization are presented. It is made an analysis for choosing the GA optimization parameters and then, the evaluation of two cases to verify the functionality of the implemented system. The results obtained illustrate an efficient methodology, capable of searching optimal solutions for proposed problems, that with the right adjustments can be of great value to accelerate the development of new aircraft.

La conception de lanceurs est un problème d’optimisation multidisciplinaire dont l’objectif est de trouverl’architecture du lanceur qui garantit une performance optimale tout en assurant un niveau de fiabilité requis.En vue de l’obtention de la solution optimale, les phases d’avant-projet sont cruciales pour le processus deconception et se caractérisent par la présence d’incertitudes dues aux phénomènes physiques impliqués etaux méconnaissances existantes sur les modèles employés. Cette thèse s’intéresse aux méthodes d’analyse et d’optimisation multidisciplinaire en présence d’incertitudes afin d’améliorer le processus de conception de lanceurs. Trois sujets complémentaires sont abordés. Tout d’abord, deux nouvelles formulations du problème de conception ont été proposées afin d’améliorer la prise en compte des interactions disciplinaires. Ensuite, deux nouvelles méthodes d’analyse de fiabilité, permettant de tenir compte d’incertitudes de natures variées, ont été proposées, impliquant des techniques d’échantillonnage préférentiel et des modèles de substitution. Enfin, une nouvelle technique de gestion des contraintes pour l’algorithme d’optimisation ”Covariance Matrix Adaptation - Evolutionary Strategy” a été développée, visant à assurer la faisabilité de la solution optimale. Les approches développées ont été comparées aux techniques proposées dans la littérature sur des cas tests d’analyse et de conception de lanceurs. Les résultats montrent que les approches proposées permettent d’améliorer l’efficacité du processus d’optimisation et la fiabilité de la solution obtenue. / Launch vehicle design is a Multidisciplinary Design Optimization problem whose objective is to find the launch vehicle architecture providing the optimal performance while ensuring the required reliability. In order to obtain an optimal solution, the early design phases are essential for the design process and are characterized by the presence of uncertainty due to the involved physical phenomena and the lack of knowledge on the used models. This thesis is focused on methodologies for multidisciplinary analysis and optimization under uncertainty for launch vehicle design. Three complementary topics are tackled. First, two new formulations have been developed in order to ensure adequate interdisciplinary coupling handling. Then, two new reliability techniques have been proposed in order to take into account the various natures of uncertainty, involving surrogate models and efficient sampling methods. Eventually, a new approach of constraint handling for optimization algorithm ”Covariance Matrix Adaptation - Evolutionary Strategy” has been developed to ensure the feasibility of the optimal solution. All the proposed methods have been compared to existing techniques in literature on analysis and design test cases of launch vehicles. The results illustrate that the proposed approaches allow the improvement of the efficiency of the design process and of the reliability of the found solution.

With the demand of computing power from electronic chips on a constant rise, innovative methods are needed for effective and efficient thermal management. Forced convection cooling through an array of micro pin-fins acts not only as a heat sink, but also allows for the electrical interconnection between stacked layers of integrated circuits. This work performs a multi-objective optimization of three shapes of pin-fins to maximize the efficiency of this cooling system. An inverse design approach that allows for the design of cooling configurations without prior knowledge of thermal mapping was proposed and validated. The optimization study showed that pin-fin configurations are capable of containing heat flux levels of next generation electronic chips. It was also shown that even under these high heat fluxes the structural integrity is not compromised. The inverse approach showed that configurations exist that are capable of cooling heat fluxes beyond those of next generation chips. Thin film heat spreaders made of diamond and graphene nano-platelets were also investigated and showed that further reduction in maximum temperature, increase in temperature uniformity and reduction in thermal stresses are possible.

ABSTRACT Multidisciplinary Design Optimization of an Extreme Aspect Ratio HALE UAV Bryan J. Morrisey Development of High Altitude Long Endurance (HALE) aircraft systems is part of a vision for a low cost communications/surveillance capability. Applications of a multi payload aircraft operating for extended periods at stratospheric altitudes span military and civil genres and support battlefield operations, communications, atmospheric or agricultural monitoring, surveillance, and other disciplines that may currently require satellite-based infrastructure. Presently, several development efforts are underway in this field, including a project sponsored by DARPA that aims at producing an aircraft that can sustain flight for multiple years and act as a pseudo-satellite. Design of this type of air vehicle represents a substantial challenge because of the vast number of engineering disciplines required for analysis, and its residence at the frontier of energy technology. The central goal of this research was the development of a multidisciplinary tool for analysis, design, and optimization of HALE UAVs, facilitating the study of a novel configuration concept. Applying design ideas stemming from a unique WWII-era project, a “pinned wing” HALE aircraft would employ self-supporting wing segments assembled into one overall flying wing. The research effort began with the creation of a multidisciplinary analysis environment comprised of analysis modules, each providing information about a specific discipline. As the modules were created, attempts were made to validate and calibrate the processes against known data, culminating in a validation study of the fully integrated MDA environment. Using the NASA / AeroVironment Helios aircraft as a basis for comparison, the included MDA environment sized a vehicle to within 5% of the actual maximum gross weight for generalized Helios payload and mission data. When wrapped in an optimization routine, the same integrated design environment shows potential for a 17.3% reduction in weight when wing thickness to chord ratio, aspect ratio, wing loading, and power to weight ratio are included as optimizer-controlled design variables. Investigation of applying the sustained day/night mission requirement and improved technology factors to the design shows that there are potential benefits associated with a segmented or pinned wing. As expected, wing structural weight is reduced, but benefits diminish as higher numbers of wing segments are considered. For an aircraft consisting of six wing segments, a maximum of 14.2% reduction in gross weight over an advanced technology optimal baseline is predicted.

Current airspace regulations require the remote pilot-in-command of an unmanned aircraft systems (UAS) to maintain visual line of sight with the vehicle for situational awareness. The future of UAS will not have these constraints as technology improves and regulations are changed. An operational model for the future of UAS is proposed where a remote operator will monitor remote vehicles with the capability to intervene if needed. One challenge facing this future operational concept is the ability for a flight data system to effectively communicate flight status to the remote operator. A system architecture has been developed to facilitate the implementation of such a flight data system. Utilizing the system architecture framework, a Phase I prototype was designed and built for two vehicles in the Autonomous Flight Laboratory (AFL) at Cal Poly. The project will continue to build on the success of Phase I, culminating in a fully functional command and control system for remote UAS operational testing.

Multidisciplinary design optimization (MDO) can be used as an effective tool to improve the design of automotive structures. Large-scale MDO problems typically involve several groups who must work concurrently and autonomously for reasons of efficiency. When performing MDO, a large number of designs need to be rated. Detailed simulation models used to assess automotive design proposals are often computationally expensive to evaluate. A useful MDO process must distribute work to the groups involved and be computationally efficient. In this thesis, MDO methods are assessed in relation to the characteristics of automotive structural applications. Single-level optimization methods have a single optimizer, while multi-level optimization methods have a distributed optimization process. Collaborative optimization and analytical target cascading are possible choices of multi-level optimization methods for automotive structures. They distribute the design process, but are complex. One approach to handle the computationally demanding simulation models involves metamodel-based design optimization (MBDO), where metamodels are used as approximations of the detailed models during optimization studies. Metamodels can be created by individual groups prior to the optimization process, and therefore also offer a way of distributing work. A single-level optimization method in combination with metamodels is concluded to be the most straightforward way of implementing MDO into the development of automotive structures.